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app.py

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+BATCH_SIZE = 64
+DOWNSAMPLE = 24
+
+import phash_jax
+import jax.numpy as jnp
+import matplotlib.pyplot as plt
+from PIL import Image
+import statistics
+import gradio
+
+def binary_array_to_hex(arr):
+	"""
+	Function to make a hex string out of a binary array.
+	"""
+	bit_string = ''.join(str(b) for b in 1 * arr.flatten())
+	width = int(jnp.ceil(len(bit_string) / 4))
+	return '{:0>{width}x}'.format(int(bit_string, 2), width=width)
+
+def compute_batch_hashes(vid_path):
+  kwargs={"width": 64, "height":64}
+  vr = VideoReader(vid_path, ctx=cpu(0), **kwargs)
+  hashes = []
+  h_prev = None
+  batch = []
+  for i in range(0, len(vr), DOWNSAMPLE * BATCH_SIZE):
+      ids = [id for id in range(i, min(i + DOWNSAMPLE * BATCH_SIZE, len(vr)), DOWNSAMPLE)]
+      vr.seek(0)
+      batch = jnp.array(vr.get_batch(ids).asnumpy())
+      batch_h =  phash_jax.batch_phash(batch)
+      for i in range(len(ids)):
+        h = batch_h[i]
+        if h_prev == None:
+          h_prev=h
+        hashes.append({"frame_id":ids[i], "hash": binary_array_to_hex(h), "distance": int(phash_jax.hash_dist(h, h_prev))})
+        h_prev = h
+  return gradio.update(value=hashes, visible=False)
+
+def plot_hash_distance(hashes, threshold):
+  fig = plt.figure()
+  ids = [h["frame_id"] for h in hashes]
+  distances = [h["distance"] for h in hashes]
+  plt.plot(ids, distances, ".")
+  plt.plot(ids, [threshold]* len(ids), "r-")
+  return fig
+
+def compute_threshold(hashes):
+  min_length = 24 * 3
+  ids = [h["frame_id"] for h in hashes]
+  distances = [h["distance"] for h in hashes]
+  thrs_ = sorted(list(set(distances)),reverse=True)
+  best = thrs_[0] - 1
+  for threshold in thrs_[1:]:
+    durations = []
+    i_start=0
+    for i, h in enumerate(hashes):
+      if h["distance"] > threshold and hashes[i-1]["frame_id"] -  hashes[i_start]["frame_id"] > min_length:
+        durations.append(hashes[i-1]["frame_id"] - hashes[i_start]["frame_id"])
+        i_start=i
+    if len(durations) < (len(hashes) * DOWNSAMPLE / 24) / 20:
+      best = threshold
+  return best
+
+def get_slides(vid_path, hashes, threshold):
+    min_length = 24 * 1.5
+    vr = VideoReader(vid_path, ctx=cpu(0))
+    slideshow = []
+    i_start = 0
+    for i, h in enumerate(hashes):
+        if h["distance"] > threshold and hashes[i-1]["frame_id"] -  hashes[i_start]["frame_id"] > min_length:
+            path=f'{FOLDER_PATH}/{vid_path.split("/")[-1].split(".")[0]}_{i_start}_{i-1}.png'
+            Image.fromarray(vr[hashes[i-1]["frame_id"]].asnumpy()).save(path)
+            slideshow.append({"slide": path, "start": i_start, "end": i-1})
+            i_start=i
+    path=f'{FOLDER_PATH}/{vid_path.split("/")[-1].split(".")[0]}_{i_start}_{len(vr)-1}.png'
+    Image.fromarray(vr[-1].asnumpy()).save(path)
+    slideshow.append({"slide": path, "start": i_start, "end": len(vr)-1})
+    return [s["slide"] for s in slideshow]
+
+def trigger_plots(f2f_distance_plot, hashes, threshold):
+  # if not hist_plot.get_config()["visible"] and len(hashes.get_config()["value"]) > 0 :
+  return gradio.update(value=plot_hash_distance(hashes, threshold))
+
+def set_visible():
+  return gradio.update(visible=True)
+  
+demo = gradio.Blocks(analytics_enabled=True)
+
+with demo:
+    with gradio.Row():
+        with gradio.Column():
+          with gradio.Row():
+            vid=gradio.Video(mirror_webcam=False)
+          with gradio.Row():
+            btn_vid_proc = gradio.Button("Compute hashes")
+          with gradio.Row():
+            hist_plot = gradio.Plot(label="Frame to frame hash distance histogram", visible=False)
+        with gradio.Column():
+            hashes = gradio.JSON()
+            with gradio.Column(visible=False) as result_row:
+              btn_plot = gradio.Button("Plot & compute optimal threshold")
+              threshold = gradio.Slider(minimum=1, maximum=30, value=5, label="Threshold")
+              f2f_distance_plot = gradio.Plot(label="Frame to frame hash distance")
+              btn_slides = gradio.Button("Extract Slides")
+    with gradio.Row():
+      slideshow = gradio.Gallery(label="Extracted slides")
+      slideshow.style(grid=6)
+    btn_vid_proc.click(fn=compute_batch_hashes, inputs=[vid], outputs=[hashes])
+    hashes.change(fn=set_visible, inputs=[], outputs=[result_row])
+    btn_plot.click(fn=compute_threshold, inputs=[hashes], outputs=[threshold])
+    btn_plot.click(fn=trigger_plots, inputs=[f2f_distance_plot, hashes, threshold], outputs=[f2f_distance_plot])
+    threshold.change(fn=plot_hash_distance, inputs=[hashes, threshold], outputs=f2f_distance_plot)
+    btn_slides.click(fn=get_slides, inputs=[vid, hashes, threshold], outputs=[slideshow])
+
+demo.launch(debug=True)